Events aggregation.
Instead of relying on raw asynchronous events, recent literature has shifted toward aggregating events together to build synchronous events representation. Common approaches range from simply integrating batch of events (constant-count) to representations involving stochastic modelling of events [1]} and temporal sparsity [2]}. As temporal information is critical in 3D human pose estimation [3]}, our first question is to understand if 3D Human Pose Estimation benefits from specific spatio-temporal representations.
To provide an answer, we compare constant-count representation with spatio-temporal voxel grids [2]}.
While constant-count simply aggregates a constant number of events into an image, spatio-temporal voxel-grid preserves the timestamp contribution of events by building \(B\)  temporal bins and have been already adopted in image reconstruction [5]}, [6]} and depth estimation [7]}. Given a set of N events \(\lbrace (\mathbf {x_k}, t_k, p_k)\rbrace _{k=0 \dots N}\) , we compute \(t_k^*\)  as the normalized timestamp of event \(k\)  into range \([0, B-1]\) . Each event \((\mathbf {x_k}, t_k, p_k)\)  contribute to each bin \(B\)  of voxel \(V\)  proportionally to its normalized timestamp \(t_k^*\) , as:
\(\begin{split}\text{V}(\mathbf {x}, t) = \sum _{k=0}^N p_k \max (0, 1 - |t - t_k^*|),\\\text{ where } t_k^* \doteq \frac{B-1}{t_N - t_0}.\end{split}\) 
